suppressMessages(library(Seurat))
suppressMessages(library(ggplot2))
suppressMessages(library(scran))
suppressMessages(library(scater))
srt <- readRDS("/mnt/nmorais-nfs/marta/pB_joana/pC_data/srt-all-samples-after-qc.rds")
srt
## An object of class Seurat 
## 16864 features across 40956 samples within 1 assay 
## Active assay: RNA (16864 features, 3000 variable features)
##  3 dimensional reductions calculated: pca, umap, tsne
table(srt$sample)
## 
## old0 old1 old3 old5  yg0  yg1  yg3  yg5 
## 6199 5596 4650 3995 5524 4434 5550 5008
colorClusters <- c("#6fccc1", "#cf1fa7", "#37ee7c", "#da0322", "#ffd94a", "#207ede", "#fea111", "#b09de9",
                   "#b86fbb", "#150de2", "#ab3f2f", "#d49b6c", "#773aab", "#759474", "#e4b2b8", "#f88a40",
                   "#74b5cf", "#e6208e", "#0a3535", "#5b37c3", "#cc4025", "#d2dc7c", "#344d76", "#ba8b07",
                   "#2e10b6", "#4d0cb0", "#e1668a", "#47a58b", "#d7734a", "#ff58c7", "#edceef", "#a21d59",
                   "#8c8da5", "#ff3e5e", "#688d4b", "#214fca", "#48a8f5", "#752758", "#3b4a4a", "#11674b",
                   "#9a1bd9", "#16146d", "#7d7277", "#2d8cce", "#2460b8", "#0f9a8f", "#b11e90", "#54ed4f",
                   "#987217", "#980119", "#cf0606", "#dd182c", "#88a1e4", "#3b89ba", "#12defd", "#feba3e",
                   "#e31fa5", "#7b3537", "#098a95", "#1f1716", "#df6c94", "#9965ee", "#b438f9", "#0140f5",
                   "#e6c4a8", "#d94f7c", "#0dae56", "#b86d1d", "#0577ad", "#464551", "#6b0959", "#ccf705",
                   "#12271d")

Annotate cells

cell_type <- srt$RNA_snn_res.0.4
cell_type <- gsub("^20$", "Unknown 2" , cell_type)
cell_type <- gsub("^19$","Plasma cells" , cell_type)
cell_type <- gsub("^18$", "Unknown 1" , cell_type)
cell_type <- gsub("^17$", "NK Cells" , cell_type)
cell_type <- gsub("^16$", "mregDCs" , cell_type)
cell_type <- gsub("^15$", "CD11a Myeloid" , cell_type)
cell_type <- gsub("^14$", "Fibrocytes" , cell_type)
cell_type <- gsub("^13$", "Granulocytes" , cell_type)
cell_type <- gsub("^12$", "Proliferating" , cell_type)
cell_type <- gsub("^11$", "B Cells" , cell_type)
cell_type <- gsub("^10$", "ILC2" , cell_type)
cell_type <- gsub("^9$","T Cells" , cell_type)
cell_type <- gsub("^8$","Eosinophils" , cell_type)
cell_type <- gsub("^7$", "cDC1" , cell_type)
cell_type <- gsub("^6$", "DCs injury" , cell_type)
cell_type <- gsub("^5","DCs residentes" , cell_type)
cell_type <- gsub("^4$","Infiltrating Type 2" , cell_type)
cell_type <- gsub("^3$", "Macrophages resident",  cell_type)
cell_type <- gsub("^2$","Infiltrating Type 1" , cell_type)
cell_type <- gsub("^1$", "Macrophages Repair" , cell_type)
cell_type <- gsub("^0$", "Macrophages intermediate" , cell_type)
table( srt$RNA_snn_res.0.4, cell_type)
##     cell_type
##      B Cells CD11a Myeloid cDC1 DCs injury DCs residentes Eosinophils
##   0        0             0    0          0              0           0
##   1        0             0    0          0              0           0
##   2        0             0    0          0              0           0
##   3        0             0    0          0              0           0
##   4        0             0    0          0              0           0
##   5        0             0    0          0           2130           0
##   6        0             0    0       1705              0           0
##   7        0             0 1299          0              0           0
##   8        0             0    0          0              0        1053
##   9        0             0    0          0              0           0
##   10       0             0    0          0              0           0
##   11     916             0    0          0              0           0
##   12       0             0    0          0              0           0
##   13       0             0    0          0              0           0
##   14       0             0    0          0              0           0
##   15       0           339    0          0              0           0
##   16       0             0    0          0              0           0
##   17       0             0    0          0              0           0
##   18       0             0    0          0              0           0
##   19       0             0    0          0              0           0
##   20       0             0    0          0              0           0
##     cell_type
##      Fibrocytes Granulocytes ILC2 Infiltrating Type 1 Infiltrating Type 2
##   0           0            0    0                   0                   0
##   1           0            0    0                   0                   0
##   2           0            0    0                5579                   0
##   3           0            0    0                   0                   0
##   4           0            0    0                   0                3924
##   5           0            0    0                   0                   0
##   6           0            0    0                   0                   0
##   7           0            0    0                   0                   0
##   8           0            0    0                   0                   0
##   9           0            0    0                   0                   0
##   10          0            0  935                   0                   0
##   11          0            0    0                   0                   0
##   12          0            0    0                   0                   0
##   13          0          360    0                   0                   0
##   14        341            0    0                   0                   0
##   15          0            0    0                   0                   0
##   16          0            0    0                   0                   0
##   17          0            0    0                   0                   0
##   18          0            0    0                   0                   0
##   19          0            0    0                   0                   0
##   20          0            0    0                   0                   0
##     cell_type
##      Macrophages intermediate Macrophages Repair Macrophages resident mregDCs
##   0                      9304                  0                    0       0
##   1                         0               5873                    0       0
##   2                         0                  0                    0       0
##   3                         0                  0                 4516       0
##   4                         0                  0                    0       0
##   5                         0                  0                    0       0
##   6                         0                  0                    0       0
##   7                         0                  0                    0       0
##   8                         0                  0                    0       0
##   9                         0                  0                    0       0
##   10                        0                  0                    0       0
##   11                        0                  0                    0       0
##   12                        0                  0                    0       0
##   13                        0                  0                    0       0
##   14                        0                  0                    0       0
##   15                        0                  0                    0       0
##   16                        0                  0                    0     303
##   17                        0                  0                    0       0
##   18                        0                  0                    0       0
##   19                        0                  0                    0       0
##   20                        0                  0                    0       0
##     cell_type
##      NK Cells Plasma cells Proliferating T Cells Unknown 1 Unknown 2
##   0         0            0             0       0         0         0
##   1         0            0             0       0         0         0
##   2         0            0             0       0         0         0
##   3         0            0             0       0         0         0
##   4         0            0             0       0         0         0
##   5         0            0             0       0         0         0
##   6         0            0             0       0         0         0
##   7         0            0             0       0         0         0
##   8         0            0             0       0         0         0
##   9         0            0             0     963         0         0
##   10        0            0             0       0         0         0
##   11        0            0             0       0         0         0
##   12        0            0           854       0         0         0
##   13        0            0             0       0         0         0
##   14        0            0             0       0         0         0
##   15        0            0             0       0         0         0
##   16        0            0             0       0         0         0
##   17      245            0             0       0         0         0
##   18        0            0             0       0       148         0
##   19        0          104             0       0         0         0
##   20        0            0             0       0         0        65
srt$cell_type <- cell_type
colors <- c("deeppink", "darkorange4", "blueviolet", "darkorchid1",
            "darkmagenta", "tomato", "lightgoldenrod3", "sienna3",
            "green3", "red", "yellow", "green4", "blue", "navy", "mediumpurple4",
            "grey", "orange3", "black", "orange", "rosybrown1", "rosybrown2"
            )
DimPlot(srt, reduction = "tsne", group.by = "cell_type", cols = colors, pt.size = 1)

Subset macrophages

unique(cell_type)
##  [1] "Macrophages intermediate" "Proliferating"           
##  [3] "Infiltrating Type 1"      "Macrophages Repair"      
##  [5] "DCs injury"               "cDC1"                    
##  [7] "Infiltrating Type 2"      "mregDCs"                 
##  [9] "Unknown 1"                "T Cells"                 
## [11] "CD11a Myeloid"            "DCs residentes"          
## [13] "Macrophages resident"     "Eosinophils"             
## [15] "B Cells"                  "Fibrocytes"              
## [17] "ILC2"                     "Granulocytes"            
## [19] "Unknown 2"                "NK Cells"                
## [21] "Plasma cells"
keep_macs <- c("Macrophages intermediate", "Macrophages Repair", "Infiltrating Type 2",
               "Infiltrating Type 1", "CD11a Myeloid", "Macrophages resident"  )
srt_mac <- srt[, srt$cell_type %in% keep_macs ]
srt_mac
## An object of class Seurat 
## 16864 features across 29535 samples within 1 assay 
## Active assay: RNA (16864 features, 3000 variable features)
##  3 dimensional reductions calculated: pca, umap, tsne
table(srt_mac$cell_type)
## 
##            CD11a Myeloid      Infiltrating Type 1      Infiltrating Type 2 
##                      339                     5579                     3924 
## Macrophages intermediate       Macrophages Repair     Macrophages resident 
##                     9304                     5873                     4516
mac_colors <- c("darkorange4","red", "yellow", "green4", "blue", "navy" )
DimPlot(srt_mac, reduction = "tsne", group.by = "cell_type", cols = colors <- mac_colors, pt.size = 1)

srt_macs <- srt[,srt$cell_type %in% keep_macs]
srt_macs
## An object of class Seurat 
## 16864 features across 29535 samples within 1 assay 
## Active assay: RNA (16864 features, 3000 variable features)
##  3 dimensional reductions calculated: pca, umap, tsne
sce <- readRDS("/mnt/nmorais-nfs/marta/pB_joana/pC_data/sce_all-samples-after-qc.rds")
sce$cell_type <- srt$cell_type
sce_macs <- sce[, sce$cell_type %in% keep_macs]
assays(sce_macs)$logcounts <- NULL
assays(sce_macs)$limma <- NULL
sce_macs
## class: SingleCellExperiment 
## dim: 16864 29535 
## metadata(0):
## assays(1): counts
## rownames(16864): Xkr4 Mrpl15 ... CAAA01147332.1 AC149090.1
## rowData names(0):
## colnames(29535): AAACCCAAGAGAGGGC-1 AAACCCAAGCACGTCC-1 ...
##   TTTGTTGGTCTGATAC-1 TTTGTTGTCGCCGAGT-1
## colData names(18): total_counts log10_total_counts ... doublet
##   cell_type
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
mat <- assays(sce_macs)$counts
gene_sum <- rowSums(mat)
gene_sum[1:5]
##   Xkr4 Mrpl15 Lypla1  Tcea1  Rgs20 
##    175  11654  12032  39067    300
summary(gene_sum)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##        0      380     2678    20860    12373 11991799
dim(mat)
## [1] 16864 29535
mat <- mat[gene_sum > 0,]
dim(mat)
## [1] 16855 29535
sce_macs <- sce_macs[gene_sum > 0,]
sce_macs
## class: SingleCellExperiment 
## dim: 16855 29535 
## metadata(0):
## assays(1): counts
## rownames(16855): Xkr4 Mrpl15 ... CAAA01147332.1 AC149090.1
## rowData names(0):
## colnames(29535): AAACCCAAGAGAGGGC-1 AAACCCAAGCACGTCC-1 ...
##   TTTGTTGGTCTGATAC-1 TTTGTTGTCGCCGAGT-1
## colData names(18): total_counts log10_total_counts ... doublet
##   cell_type
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
total_counts <- colSums(mat)
total_counts[1:5]
## AAACCCAAGAGAGGGC-1 AAACCCAAGCACGTCC-1 AAACCCACAATTGTGC-1 AAACCCAGTTAAACAG-1 
##              10496              29521              28294               7699 
## AAACCCATCATACAGC-1 
##              13169
total_features <- mat > 0
total_features <- colSums(total_features)
total_features[1:5]
## AAACCCAAGAGAGGGC-1 AAACCCAAGCACGTCC-1 AAACCCACAATTGTGC-1 AAACCCAGTTAAACAG-1 
##               3185               5187               5353               2592 
## AAACCCATCATACAGC-1 
##               3494
logmat <- log2(mat+1)
## Warning in asMethod(object): sparse->dense coercion: allocating vector of size
## 3.7 GiB
df1 <- data.frame(total_counts = total_counts,
                 total_features = total_features,
                 sample = sce_macs$sample,
                 sorting_day = sce_macs$sorting_day,
                 lane = sce_macs$lane)

df2 <- data.frame(log2_total_counts = log2(total_counts+1),
                  log2_total_features = log2(total_features+1),
                  sample = sce_macs$sample)
varMatrix <- getVarianceExplained(logmat, variables = df1)
plotExplanatoryVariables(
            varMatrix,
            variables = variables)  + 
  ggtitle("Macrophages before normalization") +
  theme(text = element_text(size=20)) +
 scale_color_manual(values=c( "dodgerblue" ,"purple" , "green3","orange", "blue"))
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Removed 2 rows containing non-finite values (`stat_density()`).

make_barplot <- function(df, x, y, title, ylab){
  ggplot(df, aes(x=x, y=y, fill=sample)) +
  
  geom_boxplot(show.legend = FALSE) + 
  theme_bw() +
  theme(text = element_text(size=20), axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  ylab(ylab) + xlab("Sample") +
  #scale_fill_manual(values=sample_colors) +
    ggtitle(title)
}
make_barplot(df1, df1$sample, df1$total_counts, "Macrophages before norm", "Library size")

make_barplot(df2, df2$sample, df2$log2_total_counts, "Macrophages before norm", "Library size (log2)")

make_barplot(df1, df1$sample, df1$total_features, "Macrophages before norm", "Total features")

make_barplot(df2, df2$sample, df2$log2_total_features, "Macrophages before norm", "Total features (log2)")

qclust <- quickCluster(sce_macs)
sce_macs <- computeSumFactors(sce_macs, clusters=qclust)      
summary(sizeFactors(sce_macs))   
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1596  0.6644  0.9307  1.0000  1.2514  4.7143
sce_macs <- logNormCounts(sce_macs, 
                     size_factors = sizeFactors(sce_macs),
                     log = TRUE,
                     pseudo_count = 1, 
                     exprs_values = "counts"
                     )
mat <- assays(sce_macs)$logcounts
total_counts <- colSums((2**mat)-1)
## Warning in asMethod(object): sparse->dense coercion: allocating vector of size
## 3.7 GiB
total_counts[1:5]
## AAACCCAAGAGAGGGC-1 AAACCCAAGCACGTCC-1 AAACCCACAATTGTGC-1 AAACCCAGTTAAACAG-1 
##           11007.69           11972.51           10600.48           10973.16 
## AAACCCATCATACAGC-1 
##           11626.40
df3 <- data.frame(total_counts = total_counts,
                 sample = sce_macs$sample,
                 sorting_day = sce_macs$sorting_day,
                 lane = sce_macs$lane,
                 total_features = total_features)

df4 <- data.frame(
                 log2_total_counts = log2(total_counts),
                 log2_total_features = log2(total_features),
                 sample = sce_macs$sample,
                 sorting_day = sce_macs$sorting_day,
                 lane = sce_macs$lane)
make_barplot(df3, df3$sample, df3$total_counts, "Macrophages after norm", "Library size") #+ylim(0, 60000)

make_barplot(df4, df4$sample, df4$log2_total_counts, "Macrophages after norm", "Library size (log2)") +ylim(11,16)

normVarMatrix <- getVarianceExplained(assays(sce_macs)$logcounts, variables = df3[,c("total_counts", "total_features", "sample", "sorting_day", "lane")])
          plotExplanatoryVariables(
            normVarMatrix,
            variables = variables)  + 
  ggtitle("Macrophages after norm") +
  theme(text = element_text(size=20)) +
 scale_color_manual(values=c( "green3","purple" ,"orange", "dodgerblue" , "blue"))
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

saveRDS(sce_macs, "/mnt/nmorais-nfs/marta/pB_joana/pC_data/sce_macs_from_raw.rds")
norm_mat <- (2**assays(sce_macs)$logcounts)-1
## Warning in asMethod(object): sparse->dense coercion: allocating vector of size
## 3.7 GiB
ln_mat <- log(norm_mat + 1)
srt_subset <- CreateSeuratObject(counts = ln_mat, min.cells = 0, min.features = 0)
## Warning: Non-unique features (rownames) present in the input matrix, making
## unique
## Warning: Non-unique cell names (colnames) present in the input matrix, making
## unique
srt_subset
## An object of class Seurat 
## 16855 features across 29535 samples within 1 assay 
## Active assay: RNA (16855 features, 0 variable features)
srt_subset <- FindVariableFeatures(srt_subset, selection.method = "vst", nfeatures = 3000)
# Identify the 20 most highly variable genes
top20 <- head(VariableFeatures(srt_subset), 20)

# plot variable features with and without labels
plot1 <- VariableFeaturePlot(srt_subset)
plot2 <- LabelPoints(plot = plot1, points = top20, repel = TRUE)
## When using repel, set xnudge and ynudge to 0 for optimal results
plot2

all.genes <- rownames(srt_subset)
srt_subset <- ScaleData(srt_subset, features = all.genes)
## Centering and scaling data matrix
srt_subset <- RunPCA(srt_subset, features = VariableFeatures(object = srt_subset), npcs = 100)
## PC_ 1 
## Positive:  Lgals3, Pkm, Clec4d, Thbs1, Tmsb10, S100a11, Clec4e, Cstb, Gapdh, Chil3 
##     Mdm2, S100a4, Rnh1, Fn1, S100a6, Nme2, F10, Vcan, Adam8, Anxa2 
##     Plac8, Capg, Prdx5, Pim1, Cd52, Txn1, Pgk1, Por, Ifitm3, Emilin2 
## Negative:  Selenop, Tanc2, Frmd4b, C1qa, Cd81, Pltp, Zfhx3, Igfbp4, Gas6, Zbtb20 
##     Serinc3, Fcgrt, Trf, C1qc, Lyve1, Zdhhc14, Cd163, Hspa1a, Hspa1b, Arhgef3 
##     Kitl, Zswim6, Mtss1, Abca9, Mgl2, C1qb, Timp2, Slc9a9, Glul, Zfp36l1 
## PC_ 2 
## Positive:  Nfkb1, Rbpj, Cflar, Marcksl1, Ehd1, Ahnak, Cxcl2, Kdm6b, Plek, Nfkbia 
##     Nfkbiz, Zfp36, Eps8, F13a1, Cebpb, Nlrp3, Txnrd1, Ccl9, Birc3, Sept11 
##     Rapgef2, Pde4b, Cd14, Clec4e, Neat1, Tnf, Cdk14, Cfh, Ifrd1, Tnfsf9 
## Negative:  Ctss, Trem2, Ms4a7, Syngr1, Hexb, Psap, Lgals3bp, Apoe, Ctsh, Tmem86a 
##     Gpnmb, Itm2b, Mpeg1, Aif1, Gngt2, Ctsb, Ctsd, Abcg1, Bst2, Cd300c2 
##     Lst1, Pld3, Grn, Tyrobp, Fabp5, Rgs2, Creg1, Ptms, Cd68, Cd63 
## PC_ 3 
## Positive:  Pf4, Ctsb, Cd63, Ctsd, Arhgap10, Emp1, Lgals1, Ctsl, Grn, Vat1 
##     Cd36, Spp1, Fabp5, C3ar1, Cd68, Stab1, Gpnmb, Folr2, Cstb, Dab2 
##     Trem2, Slc7a8, Slc6a8, Timp2, Syngr1, Nrp2, Pdpn, Mt1, Lhfpl2, Sash1 
## Negative:  Plbd1, Gpr141, Itgal, Napsa, Ifitm6, Cytip, Plac8, Gsr, Klra2, Sell 
##     Arhgap26, Cybb, Hp, Lsp1, Ccr2, Adgre5, Cd52, Adgre4, Fgr, Sorl1 
##     Nedd9, Mir142hg, Mcemp1, Arhgap15, Cd74, Sik3, Ace, Zfp710, Stap1, Parp8 
## PC_ 4 
## Positive:  F13a1, Ccl6, S100a6, S100a4, S100a10, Ccl9, Gda, Lyve1, Anxa2, Chil3 
##     Cfp, Txnip, Cbr2, Plac8, Ifitm3, Ednrb, Vcan, Msr1, Lgals1, Capg 
##     Maf, Pdia6, Plekhg5, Fgfr1, Folr2, Stxbp6, S100a11, Hp, Dnm1, Itgam 
## Negative:  Cxcl16, Bcl2a1b, H2-Eb1, Ccrl2, H2-Aa, H2-Ab1, Cd83, Bcl2a1d, Cd74, Axl 
##     Rel, Dock10, Slamf7, Nlrp3, Nfkb1, Pde4b, Tlr2, Nfe2l2, H2-DMb1, Mthfs 
##     Tnfaip3, Tgfbr1, Tnip3, Ccl4, Tmem176b, Il1b, Arl5c, Tnfrsf1b, Tmem176a, H2-DMa 
## PC_ 5 
## Positive:  Isg15, Ifi211, Rsad2, Ifit3, Irf7, Slfn5, Ifit2, Mndal, Ifi209, Ifi204 
##     Oasl2, Ifi203, Phf11d, Phf11b, Ifi47, Ifi213, Zbp1, Rnf213, Trim30a, Ifi205 
##     Usp18, Ifi206, Parp14, Fcgr1, A330040F15Rik, Ifit1, Slfn1, Slfn4, Ccl12, Oasl1 
## Negative:  Xylt1, Cytip, Gsr, Fyn, Ace, Pde3b, Havcr2, Cd9, Itgal, Treml4 
##     Mxi1, Gngt2, Smpdl3a, Mgst1, Gpcpd1, Runx2, Antxr2, Lyst, Msrb1, Fam117b 
##     Gcnt2, Zfyve9, Nedd9, Cd244a, Sorl1, F5, Pglyrp1, Klhl2, Stap1, Mrtfa
ElbowPlot(srt_subset, ndims = 100) + 
  geom_vline(xintercept = 25, color = "red", alpha = 0.5) + 
  theme(text = element_text(size = 10), 
        axis.text.x = element_text(size = 10),
        axis.text.y = element_text(size = 10))

srt_subset$sample <- sce_macs$sample
srt_subset$lane <- sce_macs$lane
srt_subset$sorting_day <- sce_macs$sorting_day
srt_subset$cell_type <- sce_macs$cell_type
DimPlot(srt_subset, reduction = "pca", group.by = "cell_type", cols = mac_colors)

srt_subset <- RunUMAP(srt_subset, n.components = 10, features = VariableFeatures(srt_subset))
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 16:45:37 UMAP embedding parameters a = 0.9922 b = 1.112
## 16:45:37 Read 29535 rows and found 3000 numeric columns
## 16:45:37 Using Annoy for neighbor search, n_neighbors = 30
## 16:45:37 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 16:46:11 Writing NN index file to temp file /tmp/Rtmpc4sgW7/filedba455ec40f2
## 16:46:11 Searching Annoy index using 1 thread, search_k = 3000
## 16:53:04 Annoy recall = 100%
## 16:53:05 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 16:53:07 Initializing from normalized Laplacian + noise (using irlba)
## 16:53:08 Commencing optimization for 200 epochs, with 1608828 positive edges
## 16:53:37 Optimization finished
DimPlot(srt_subset, reduction = "umap", group.by = "cell_type", cols = mac_colors)

srt_subset <- RunTSNE(srt_subset,
             dim.embed = 3,
            dims = 1:25)
DimPlot(srt_subset, reduction = "tsne", group.by = "cell_type", cols = mac_colors)

saveRDS(srt_subset, "/mnt/nmorais-nfs/marta/pB_joana/pC_data/srt-mono-macs-from-raw.rds")
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] scater_1.22.0               scran_1.22.1               
##  [3] scuttle_1.4.0               SingleCellExperiment_1.16.0
##  [5] SummarizedExperiment_1.24.0 Biobase_2.54.0             
##  [7] GenomicRanges_1.46.1        GenomeInfoDb_1.30.1        
##  [9] IRanges_2.28.0              S4Vectors_0.32.4           
## [11] BiocGenerics_0.40.0         MatrixGenerics_1.6.0       
## [13] matrixStats_0.63.0          ggplot2_3.4.1              
## [15] SeuratObject_4.1.3          Seurat_4.1.1               
## 
## loaded via a namespace (and not attached):
##   [1] plyr_1.8.8                igraph_1.3.5             
##   [3] lazyeval_0.2.2            sp_1.6-0                 
##   [5] splines_4.1.2             BiocParallel_1.28.3      
##   [7] listenv_0.9.0             scattermore_0.8          
##   [9] digest_0.6.31             htmltools_0.5.4          
##  [11] viridis_0.6.2             fansi_1.0.4              
##  [13] magrittr_2.0.3            ScaledMatrix_1.2.0       
##  [15] tensor_1.5                cluster_2.1.4            
##  [17] ROCR_1.0-11               limma_3.50.3             
##  [19] globals_0.16.2            spatstat.sparse_3.0-0    
##  [21] colorspace_2.1-0          ggrepel_0.9.3            
##  [23] xfun_0.37                 dplyr_1.1.0              
##  [25] RCurl_1.98-1.10           jsonlite_1.8.4           
##  [27] progressr_0.13.0          spatstat.data_3.0-0      
##  [29] survival_3.5-0            zoo_1.8-11               
##  [31] glue_1.6.2                polyclip_1.10-4          
##  [33] gtable_0.3.1              zlibbioc_1.40.0          
##  [35] XVector_0.34.0            leiden_0.4.3             
##  [37] DelayedArray_0.20.0       BiocSingular_1.10.0      
##  [39] future.apply_1.10.0       abind_1.4-5              
##  [41] scales_1.2.1              edgeR_3.36.0             
##  [43] spatstat.random_3.1-3     miniUI_0.1.1.1           
##  [45] Rcpp_1.0.10               viridisLite_0.4.1        
##  [47] xtable_1.8-4              dqrng_0.3.0              
##  [49] reticulate_1.26           spatstat.core_2.4-4      
##  [51] rsvd_1.0.5                metapod_1.2.0            
##  [53] htmlwidgets_1.6.1         httr_1.4.4               
##  [55] RColorBrewer_1.1-3        ellipsis_0.3.2           
##  [57] ica_1.0-3                 farver_2.1.1             
##  [59] pkgconfig_2.0.3           sass_0.4.5               
##  [61] uwot_0.1.14               deldir_1.0-6             
##  [63] locfit_1.5-9.7            utf8_1.2.3               
##  [65] labeling_0.4.2            tidyselect_1.2.0         
##  [67] rlang_1.0.6               reshape2_1.4.4           
##  [69] later_1.3.0               munsell_0.5.0            
##  [71] tools_4.1.2               cachem_1.0.6             
##  [73] cli_3.6.0                 generics_0.1.3           
##  [75] ggridges_0.5.4            evaluate_0.20            
##  [77] stringr_1.5.0             fastmap_1.1.0            
##  [79] yaml_2.3.7                goftest_1.2-3            
##  [81] knitr_1.42                fitdistrplus_1.1-8       
##  [83] purrr_1.0.1               RANN_2.6.1               
##  [85] pbapply_1.7-0             future_1.31.0            
##  [87] nlme_3.1-162              sparseMatrixStats_1.6.0  
##  [89] mime_0.12                 compiler_4.1.2           
##  [91] rstudioapi_0.14           beeswarm_0.4.0           
##  [93] plotly_4.10.1             png_0.1-8                
##  [95] spatstat.utils_3.0-1      statmod_1.5.0            
##  [97] tibble_3.1.8              bslib_0.4.2              
##  [99] stringi_1.7.12            highr_0.10               
## [101] bluster_1.4.0             lattice_0.20-45          
## [103] Matrix_1.5-3              vctrs_0.5.2              
## [105] pillar_1.8.1              lifecycle_1.0.3          
## [107] spatstat.geom_3.0-6       lmtest_0.9-40            
## [109] jquerylib_0.1.4           BiocNeighbors_1.12.0     
## [111] RcppAnnoy_0.0.20          data.table_1.14.6        
## [113] cowplot_1.1.1             bitops_1.0-7             
## [115] irlba_2.3.5.1             httpuv_1.6.8             
## [117] patchwork_1.1.2           R6_2.5.1                 
## [119] promises_1.2.0.1          KernSmooth_2.23-20       
## [121] gridExtra_2.3             vipor_0.4.5              
## [123] parallelly_1.34.0         codetools_0.2-18         
## [125] MASS_7.3-58.2             withr_2.5.0              
## [127] sctransform_0.3.5         GenomeInfoDbData_1.2.7   
## [129] mgcv_1.8-41               parallel_4.1.2           
## [131] beachmat_2.10.0           grid_4.1.2               
## [133] rpart_4.1.19              tidyr_1.3.0              
## [135] rmarkdown_2.20            DelayedMatrixStats_1.16.0
## [137] Rtsne_0.16                shiny_1.7.4              
## [139] ggbeeswarm_0.7.1